Veal calves management in Québec, Canada: Part I. Association between passive immunity transfer at arrival and health disorders
Bibliographic record
Abstract
Morbidity and mortality remain important concerns for veal production which traditionally involves very young calves being transported directly from dairy farms to calf-raising facilities or sold through auction markets from dairy farms. There are few studies that examine the relationship between transfer of passive immunity and health outcomes in veal production in Québec. The objective of this study was to investigate the risk factors associated with morbidity and mortality in milk and grain-fed veal facilities in Québec. Between October 2017 and December 2018, a prospective cohort study was conducted on 59 batches of milk- and grain-fed veal calves from Montérégie and Centre du Québec regions in Québec, Canada. A total of 30 calves per batch were randomly sampled for the transfer of passive immunity (TPI) status using the Brix refractometer (serum threshold ≥ 8.4%). Throughout the production cycle, arrival, treatment, mortality, and slaughter records of each batch of calves were extracted and used to quantify individual- and group-level risk factors. Morbidity and mortality were modelled through generalized linear mixed models (GLMMs) using a logit link, as function of categorical variables (individual inadequate TPI, arrival season, purchasing sites, and arrival weights) and a continuous variable (proportion of inadequate TPI in the batch). Given that the health and performance data are usually collected retrospectively under commercial settings, incomplete data was recognized as a potential issue. Thus, multiple imputation models were used. From 59 batches where 1729 calves were sampled for measuring TPI, 1084 calves had a serum Brix% < 8.4% giving a prevalence of 62.7% of inadequate TPI. The prevalence of morbidity and mortality in those 59 batches were not associated with the batch-level inadequate TPI prevalence. On the other hand, individual calves with inadequate TPI had higher odds of morbidity (OR: 1.56; 95% CI: 1.2-2.05, P = 0.001) compared to those calves with adequate TPI. Odds of mortality, on the other hand, were not significantly affected by inadequate TPI (OR:1.27; 95% CI: 0.74-2.18, P = 0.3). Calves arriving to the facility during the winter season had lower odds of mortality than those arriving in fall (OR:0.16; 95% CI: 0.03-0.78, P = 0.02). In conclusion, this study suggests that inadequate TPI, as assessed upon arrival in veal farms, remains an important concern for negative health outcomes in veal calves.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".